Abstract In recent years, there has been a growing interest in synthetic data for several computer vision applications, such as automotive, detection and tracking, surveillance, medical image analysis and robotics. Early use of synthetic data was aimed at performing controlled experiments under the analysis by synthesis approach. Currently, synthetic data are mainly used for training computer vision models, especially deep learning ones, to address well-known issues of real data, such as manual annotation effort, data imbalance and bias, and privacy-related restrictions. In this work, we survey the use of synthetic training data focusing on applications related to video surveillance, whose relevance has rapidly increased in the past few years due to their connection to security: crowd counting, object and pedestrian detection and tracking, behaviour analysis, person re-identification and face recognition. Synthetic training data are even more interesting in this kind of application, to address further, specific issues arising, e.g., from typically unconstrained image or video acquisition conditions and cross-scene application scenarios. We categorise and discuss the existing methods for creating synthetic data, analyse the synthetic data sets proposed in the literature for each of the considered applications, and provide an overview of their effectiveness as training data. We finally discuss whether and to what extent the existing synthetic data sets mitigate the issues of real data, highlight existing open issues, and suggest future research directions in this field.

Person re-identification is a challenging cross-camera matching problem, which is inherently subject to domain shift. To mitigate it, many solutions have been proposed so far, based on four kinds of approaches: supervised and unsupervised domain adaptation, direct transfer, and domain generalisation; in particular, the first two approaches require target data during system design, respectively labelled and unlabelled. In this work, we consider a very different approach, known as human-in-the-loopHITL), which consists of exploiting user’s feedback on target data processed during system operation to improve re-identification accuracy. Although it seems particularly suited to this application, given the inherent interaction with a human operator, HITL methods have been proposed for person re-identification by only a few works so far, and with a different purpose than addressing domain shift. However, we argue that HITL deserves further consideration in person re-identification, also as a potential alternative solution against domain shift. To substantiate our view, we consider simple HITL implementations which do not require model re-training or fine-tuning: they are based on well-known relevance feedback algorithms for content-based image retrieval, and of novel versions of them we devise specifically for person re-identification. We then conduct an extensive, cross-data set experimental evaluation of our HITL implementations on benchmark data sets, and compare them with a large set of existing methods against domain shift, belonging to the four categories mentioned above. Our results provide evidence that HITL can be as effective as, or even outperform, existing ad hoc solutions against domain shift for person re-identification, even under the simple implementations we consider. We believe that these results can foster further research on HITL in the person re-identification field, where, in our opinion, its potential has not been thoroughly explored so far.

Person re-identification is a prominent topic in computer vision due to its security-related applications, and to the fact that issues such as variations in illumination, background, pedestrian pose and clothing appearance make it a very challenging task in real-world scenarios. State-of-the-art supervised methods require a huge manual annotation effort for training data and exhibit limited generalisation capability to unknown target domains. Synthetic data sets have recently been proposed as one possible solution to mitigate these problems, aimed at improving generalisation capability by encompassing a larger amount of variations in the above mentioned visual factors, with no need for manual annotation. However, existing synthetic data sets differ in many aspects, including the number of images, identities and cameras, and in their degree of photorealism, and there is not yet a clear understanding of how all such factors affect person re-identification performance. This work makes a first step towards filling this gap through an indepth empirical investigation, where we use existing synthetic data sets for model training and real benchmark ones for performance evaluation. Our results provide interesting insights towards developing effective synthetic data sets for person re-identification.

The International Fingerprint Liveness Detection Competition is an international biennial competition open to academia and industry with the aim to assess and report advances in Fingerprint Presentation Attack Detection. The proposed "Liveness Detection in Action" and "Fingerprint representation" challenges were aimed to evaluate the impact of a PAD embedded into a verification system, and the effectiveness and compactness of feature sets for mobile applications. Furthermore, we experimented a new spoof fabrication method that has particularly affected the final results. Twenty-three algorithms were submitted to the competition, the maximum number ever achieved by LivDet.

Supervised deep learning methods have recently achieved remarkable performance in person re-identification. Unsupervised domain adaptation (UDA) approaches have also been proposed for application scenarios where only unlabelled data are available from target camera views. We consider a more challenging scenario when even collecting a suitable amount of representative, unlabelled target data for offline training or fine-tuning is infeasible. In this context we revisit the human-in-the-loop (HITL) approach, which exploits online the operator's feedback on a small amount of target data. We argue that HITL is a kind of online domain adaptation specifically suited to person re-identification. We then reconsider relevance feedback methods for content-based image retrieval that are computationally much cheaper than state-of-the-art HITL methods for person reidentification, and devise a specific feedback protocol for them. Experimental results show that HITL can achieve comparable or better performance than UDA, and is therefore a valid alternative when the lack of unlabelled target data makes UDA infeasible.